Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image

Jiang, Yun; Wang, Falin; Gao, Jing; Cao, Simin

doi:10.3390/app10113777

Cited by 36 publications

(28 citation statements)

References 43 publications

(55 reference statements)

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“…In this paper, we propose a multi-scale deep learning model for drusen segmentation. While most of the previous work on medical segmentation used UNet-based architecture on the whole image [24,29,34], our method applies a multi-scale learning method to make a fine segmentation prediction. It is suitable for drusen segmentation with high-resolution fundus images.…”

Section: Discussionmentioning

confidence: 99%

Automatic Drusen Segmentation for Age-Related Macular Degeneration in Fundus Images Using Deep Learning

et al. 2020

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Drusen are the main aspect of detecting age-related macular degeneration (AMD). Ophthalmologists can evaluate the condition of AMD based on drusen in fundus images. However, in the early stage of AMD, the drusen areas are usually small and vague. This leads to challenges in the drusen segmentation task. Moreover, due to the high-resolution fundus images, it is hard to accurately predict the drusen areas with deep learning models. In this paper, we propose a multi-scale deep learning model for drusen segmentation. By exploiting both local and global information, we can improve the performance, especially in the early stages of AMD cases.

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Section: Discussionmentioning

confidence: 99%

Automatic Drusen Segmentation for Age-Related Macular Degeneration in Fundus Images Using Deep Learning

et al. 2020

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“…The efficiency of the model was validated by the performance of two segmentation processes like optic cup and disc segmentation and retinal vessel segmentation. The model achieved 99.67% accuracy for optic disc segmentation, 99.50% for optic cup segmentation, and 96.42% for retinal vessel segmentation by using the Drishti-GS1 dataset [ 14 ].…”

Section: Related Workmentioning

confidence: 99%

Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model

Sinthuja

Raja

et al. 2022

Journal of Healthcare Engineering

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Glaucoma is the second most common cause for blindness around the world and the third most common in Europe and the USA. Around 78 million people are presently living with glaucoma (2020). It is expected that 111.8 million people will have glaucoma by the year 2040. 90% of glaucoma is undetected in developing nations. It is essential to develop a glaucoma detection system for early diagnosis. In this research, early prediction of glaucoma using deep learning technique is proposed. In this proposed deep learning model, the ORIGA dataset is used for the evaluation of glaucoma images. The U-Net architecture based on deep learning algorithm is implemented for optic cup segmentation and a pretrained transfer learning model; DenseNet-201 is used for feature extraction along with deep convolution neural network (DCNN). The DCNN approach is used for the classification, where the final results will be representing whether the glaucoma infected or not. The primary objective of this research is to detect the glaucoma using the retinal fundus images, which can be useful to determine if the patient was affected by glaucoma or not. The result of this model can be positive or negative based on the outcome detected as infected by glaucoma or not. The model is evaluated using parameters such as accuracy, precision, recall, specificity, and F-measure. Also, a comparative analysis is conducted for the validation of the model proposed. The output is compared to other current deep learning models used for CNN classification, such as VGG-19, Inception ResNet, ResNet 152v2, and DenseNet-169. The proposed model achieved 98.82% accuracy in training and 96.90% in testing. Overall, the performance of the proposed model is better in all the analysis.

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“…In [85], a connection sensitive attention U-Net (CSAU) is proposed to segment retinal blood vessels. Like [80,84,86], CSAU is proposed to segment retinal blood vessels. Unlike [80,84,86], a connection sensitive loss is proposed and combines with attention gates.…”

Section: Attention U-netmentioning

confidence: 99%

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

Wu¹,

Liu²,

Li³

et al. 2022

Preprint

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Image analysis technology is used to solve the inadvertences of artificial traditional methods in disease, wastewater treatment, environmental change monitoring analysis and convolutional neural networks (CNN) play an important role in microscopic image analysis. An important step in detection, tracking, monitoring, feature extraction, modeling and analysis is image segmentation, in which U-Net has increasingly applied in microscopic image segmentation. This paper comprehensively reviews the development history of U-Net, and analyzes various research results of various segmentation methods since the emergence of U-Net and conducts a comprehensive review of related papers. First, This paper has summarizes the improved methods of U-Net and then listed the existing significances of image segmentation techniques and their improvements that has introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can clearly see the dynamics of transmission of technological development and keep up with future trends in this interdisciplinary field.

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Multi-Path Recurrent U-Net Segmentation of Retinal Fundus Image

Cited by 36 publications

References 43 publications

Automatic Drusen Segmentation for Age-Related Macular Degeneration in Fundus Images Using Deep Learning

Automatic Drusen Segmentation for Age-Related Macular Degeneration in Fundus Images Using Deep Learning

Segmentation and Classification of Glaucoma Using U-Net with Deep Learning Model

A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification

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